1. Technical Field
The present invention relates to a driver assistance system which provides a collision warning with respect to a pedestrian.
2. Description of Related Art
During the last few years camera based driver assistance systems (DAS) have been entering the market; including lane departure warning (LDW), Automatic High-beam Control (AHC), pedestrian recognition, and forward collision warning (FCW).
Lane departure warning (LDW) systems are designed to give a warning in the case of unintentional lane departure. The warning is given when the vehicle crosses or is about to cross the lane marker. Driver intention is determined based on use of turn signals, change in steering wheel angle, vehicle speed and brake activation.
In image processing, the Moravec corner detection algorithm is probably one of the earliest corner detection algorithms and defines a corner to be a point with low self-similarity. The Moravec algorithm tests each pixel in the image to see if a corner is present, by considering how similar a patch centered on the pixel is to nearby largely overlapping patches. The similarity is measured by taking the sum of squared differences squared differences (SSD) between the two patches. A lower number indicates more similarity. An alternative approach to corner detection in images is based on a method proposed by Harris and Stephens, which is an improvement of the method by Moravec. Harris and Stephens improved upon Moravec's corner detector by considering the differential of the corner score with respect to direction directly, instead of using nearby patches of Moravec.
In computer vision, a widely used differential method for optical flow estimation was developed by Bruce D. Lucas and Takeo Kanade. The Lucas-Kanade method assumes that the flow is essentially constant in a local neighborhood of the pixel under consideration, and solves the basic optical flow equations for all the pixels in that neighborhood, by the least squares criterion. By combining information from several nearby pixels, the Lucas-Kanade method can often resolve the inherent ambiguity of the optical flow equation. It is also less sensitive to image noise than point-wise methods. On the other hand, since it is a purely local method, it cannot provide flow information in the interior of uniform regions of the image.